Machine Learning Based IoT Geriatric Fall Intelligent System in Pandemic (Preprint)
UNSTRUCTURED In the current pandemic, there is lack of medical care takers and physicians in hospitals and health centers. The patients other than COVID infected are also affected by this scenario. Besides, the hospitals are also not admitting the old age peoples, and they are scared to approach hospitals even for their basic health checkups. But, they have to be cared and monitored to avoid the risk factors like fall incidence which may cause fatal injury. In such a case, this paper focuses on the cloud based IoT gadget for early fall incidence prediction. It is machine learning based fall incidence prediction system for the old age patients. The approaches such as Logistic Regression, Naive Bayes, Stochastic Gradient Descent, Decision Tree, Random Forest, Support Vector Machines, K-Nearest Neighbor and ensemble learning boosting techniques, i.e., XGBoost are used for fall incidence prediction. The proposed approach is first tested on the benchmark activity sensor data with different features for training purpose. The real-time vital signs like heart rate, blood pressure are recorded and stored in cloud and the machine learning approaches are applied to it. Then tested on the real-time sensor data like heart rate and blood pressure data of geriatric patients to predict early fall.